






'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


UpperCamelCase     :   List[str]	      =		r'\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `" / "`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `" // "`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `"train"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `"compressed"`)\n            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n            `"compressed"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a "dummy" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n'




@add_start_docstrings(a      )
class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		_UpperCamelCase						=       'rag'
		_UpperCamelCase						=       True






		def __init__(   self ,_lowerCAmelCase=None ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=" / " ,_lowerCAmelCase=" // " ,_lowerCAmelCase=5 ,_lowerCAmelCase=3_00 ,_lowerCAmelCase=7_68 ,_lowerCAmelCase=8 ,_lowerCAmelCase="wiki_dpr" ,_lowerCAmelCase="train" ,_lowerCAmelCase="compressed" ,_lowerCAmelCase=None ,_lowerCAmelCase=None ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=0.0 ,_lowerCAmelCase=True ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=True ,_lowerCAmelCase=None ,**_lowerCAmelCase ,):
								super().__init__(
								    bos_token_id=_lowerCAmelCase ,pad_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ,decoder_start_token_id=_lowerCAmelCase ,forced_eos_token_id=_lowerCAmelCase ,is_encoder_decoder=_lowerCAmelCase ,prefix=_lowerCAmelCase ,vocab_size=_lowerCAmelCase ,**_lowerCAmelCase ,)
								assert (
								    "question_encoder" in kwargs and "generator" in kwargs
								), "Config has to be initialized with question_encoder and generator config"
								lowerCamelCase__								=	kwargs.pop("""question_encoder"""							)
								lowerCamelCase__								=	question_encoder_config.pop("""model_type"""							)
								lowerCamelCase__								=	kwargs.pop("""generator"""							)
								lowerCamelCase__								=	decoder_config.pop("""model_type"""							)

								from ..auto.configuration_auto import AutoConfig

								lowerCamelCase__								=	AutoConfig.for_model(_lowerCAmelCase ,**_lowerCAmelCase							)
								lowerCamelCase__								=	AutoConfig.for_model(_lowerCAmelCase ,**_lowerCAmelCase							)

								lowerCamelCase__								=	reduce_loss
								lowerCamelCase__								=	label_smoothing
								lowerCamelCase__								=	exclude_bos_score
								lowerCamelCase__								=	do_marginalize

								lowerCamelCase__								=	title_sep
								lowerCamelCase__								=	doc_sep
								lowerCamelCase__								=	n_docs
								lowerCamelCase__								=	max_combined_length

								lowerCamelCase__								=	dataset
								lowerCamelCase__								=	dataset_split
								lowerCamelCase__								=	index_name

								lowerCamelCase__								=	retrieval_vector_size
								lowerCamelCase__								=	retrieval_batch_size
								lowerCamelCase__								=	passages_path
								lowerCamelCase__								=	index_path
								lowerCamelCase__								=	use_dummy_dataset

								lowerCamelCase__								=	output_retrieved

								lowerCamelCase__								=	do_deduplication

								lowerCamelCase__								=	use_cache

								if self.forced_eos_token_id is None:
														lowerCamelCase__								=	getattr(self.generator ,"""forced_eos_token_id""" ,_lowerCAmelCase							)






		@classmethod
		def       UpperCamelCase_       (   cls ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase							):
								return cls(question_encoder=question_encoder_config.to_dict() ,generator=generator_config.to_dict() ,**_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	copy.deepcopy(self.__dict__							)
								lowerCamelCase__								=	self.question_encoder.to_dict()
								lowerCamelCase__								=	self.generator.to_dict()
								lowerCamelCase__								=	self.__class__.model_type
								return output






'''simple docstring'''






import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin

@skip_mps
class 				a__(   lowerCamelCase__						,					lowerCamelCase__						,					unittest.TestCase      ):
    lowercase__					=       IFPipeline
    lowercase__					=       TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
    lowercase__					=       TEXT_TO_IMAGE_BATCH_PARAMS
    lowercase__					=       PipelineTesterMixin.required_optional_params - {"""latents"""}







    def      lowercase_			( self					:    List[Any]      ):
      return self._get_dummy_components()







    def      lowercase_			( self					:    int		,     __snake_case					:    List[str]		,     __snake_case					:    List[Any]=0      ):
      if str(__snake_case      ).startswith('mps'      ):
        a		:   List[Any]	  =	torch.manual_seed(__snake_case      )
      else:
        a		:   str	  =	torch.Generator(device=__snake_case      ).manual_seed(__snake_case      )

      a		:   Dict	  =	{
          'prompt': 'A painting of a squirrel eating a burger',
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }

      return inputs







    def      lowercase_			( self					:    Optional[Any]      ):
      self._test_save_load_optional_components()







    @unittest.skipIf(torch_device != 'cuda'		,     reason='float16 requires CUDA'      )
    def      lowercase_			( self					:    Union[str, Any]      ):
      # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
      super().test_save_load_floataa(expected_max_diff=1e-1      )







    def      lowercase_			( self					:    Tuple      ):
      self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )







    def      lowercase_			( self					:    str      ):
      self._test_save_load_local()







    def      lowercase_			( self					:    Any      ):
      self._test_inference_batch_single_identical(
          expected_max_diff=1e-2		,     )







    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()		,     reason='XFormers attention is only available with CUDA and `xformers` installed'		,     )
    def      lowercase_			( self					:    List[str]      ):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )

@slow
@require_torch_gpu
class 				a__(   unittest.TestCase      ):







    def      lowercase_			( self					:    Optional[Any]      ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    def      lowercase_			( self					:    Tuple      ):
      # if

      a		:   List[Any]	  =	IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0'		,     variant='fp16'		,     torch_dtype=torch.floataa      )

      a		:   Optional[int]	  =	IFSuperResolutionPipeline.from_pretrained(
          'DeepFloyd/IF-II-L-v1.0'		,     variant='fp16'		,     torch_dtype=torch.floataa		,     text_encoder=__snake_case		,     tokenizer=__snake_case      )

      # pre compute text embeddings and remove T5 to save memory

      pipe_a.text_encoder.to('cuda'      )

      a  ,      a		:   Optional[Any]	  =	pipe_a.encode_prompt('anime turtle'		,     device='cuda'      )

      del pipe_a.tokenizer
      del pipe_a.text_encoder
      gc.collect()

      a		:   List[Any]	  =	None
      a		:   Dict	  =	None

      pipe_a.enable_model_cpu_offload()
      pipe_a.enable_model_cpu_offload()

      pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
      pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

      self._test_if(__snake_case		,     __snake_case		,     __snake_case		,     __snake_case      )

      pipe_a.remove_all_hooks()
      pipe_a.remove_all_hooks()

      # img2img

      a		:   List[str]	  =	IFImgaImgPipeline(**pipe_a.components      )
      a		:   int	  =	IFImgaImgSuperResolutionPipeline(**pipe_a.components      )

      pipe_a.enable_model_cpu_offload()
      pipe_a.enable_model_cpu_offload()

      pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
      pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

      self._test_if_imgaimg(__snake_case		,     __snake_case		,     __snake_case		,     __snake_case      )

      pipe_a.remove_all_hooks()
      pipe_a.remove_all_hooks()

      # inpainting

      a		:   Tuple	  =	IFInpaintingPipeline(**pipe_a.components      )
      a		:   int	  =	IFInpaintingSuperResolutionPipeline(**pipe_a.components      )

      pipe_a.enable_model_cpu_offload()
      pipe_a.enable_model_cpu_offload()

      pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
      pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

      self._test_if_inpainting(__snake_case		,     __snake_case		,     __snake_case		,     __snake_case      )







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    Optional[int]		,     __snake_case					:    Union[str, Any]		,     __snake_case					:    Tuple		,     __snake_case					:    List[str]      ):
      # pipeline 1

      _start_torch_memory_measurement()

      a		:   int	  =	torch.Generator(device='cpu'      ).manual_seed(0      )
      a		:   int	  =	pipe_a(
          prompt_embeds=__snake_case		,     negative_prompt_embeds=__snake_case		,     num_inference_steps=2		,     generator=__snake_case		,     output_type='np'		,     )

      a		:   Dict	  =	output.images[0]

      assert image.shape == (64, 64, 3)

      a		:   Any	  =	torch.cuda.max_memory_allocated()
      assert mem_bytes < 13 * 10**9

      a		:   Optional[int]	  =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'      )
      assert_mean_pixel_difference(__snake_case		,     __snake_case      )

      # pipeline 2

      _start_torch_memory_measurement()

      a		:   List[str]	  =	torch.Generator(device='cpu'      ).manual_seed(0      )

      a		:   List[Any]	  =	floats_tensor((1, 3, 64, 64)		,     rng=random.Random(0      )      ).to(__snake_case      )

      a		:   Any	  =	pipe_a(
          prompt_embeds=__snake_case		,     negative_prompt_embeds=__snake_case		,     image=__snake_case		,     generator=__snake_case		,     num_inference_steps=2		,     output_type='np'		,     )

      a		:   Tuple	  =	output.images[0]

      assert image.shape == (2_56, 2_56, 3)

      a		:   Optional[Any]	  =	torch.cuda.max_memory_allocated()
      assert mem_bytes < 4 * 10**9

      a		:   Tuple	  =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'      )
      assert_mean_pixel_difference(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    str		,     __snake_case					:    int		,     __snake_case					:    Any		,     __snake_case					:    Any      ):
      # pipeline 1

      _start_torch_memory_measurement()

      a		:   Optional[Any]	  =	floats_tensor((1, 3, 64, 64)		,     rng=random.Random(0      )      ).to(__snake_case      )

      a		:   Optional[int]	  =	torch.Generator(device='cpu'      ).manual_seed(0      )

      a		:   Any	  =	pipe_a(
          prompt_embeds=__snake_case		,     negative_prompt_embeds=__snake_case		,     image=__snake_case		,     num_inference_steps=2		,     generator=__snake_case		,     output_type='np'		,     )

      a		:   List[Any]	  =	output.images[0]

      assert image.shape == (64, 64, 3)

      a		:   Optional[Any]	  =	torch.cuda.max_memory_allocated()
      assert mem_bytes < 10 * 10**9

      a		:   Tuple	  =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'      )
      assert_mean_pixel_difference(__snake_case		,     __snake_case      )

      # pipeline 2

      _start_torch_memory_measurement()

      a		:   Optional[int]	  =	torch.Generator(device='cpu'      ).manual_seed(0      )

      a		:   str	  =	floats_tensor((1, 3, 2_56, 2_56)		,     rng=random.Random(0      )      ).to(__snake_case      )
      a		:   Any	  =	floats_tensor((1, 3, 64, 64)		,     rng=random.Random(0      )      ).to(__snake_case      )

      a		:   Dict	  =	pipe_a(
          prompt_embeds=__snake_case		,     negative_prompt_embeds=__snake_case		,     image=__snake_case		,     original_image=__snake_case		,     generator=__snake_case		,     num_inference_steps=2		,     output_type='np'		,     )

      a		:   Optional[Any]	  =	output.images[0]

      assert image.shape == (2_56, 2_56, 3)

      a		:   Any	  =	torch.cuda.max_memory_allocated()
      assert mem_bytes < 4 * 10**9

      a		:   Dict	  =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'      )
      assert_mean_pixel_difference(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    List[Any]		,     __snake_case					:    Tuple		,     __snake_case					:    Any		,     __snake_case					:    str		,     __snake_case					:    int      ):
      # pipeline 1

      _start_torch_memory_measurement()

      a		:   Tuple	  =	floats_tensor((1, 3, 64, 64)		,     rng=random.Random(0      )      ).to(__snake_case      )
      a		:   Union[str, Any]	  =	floats_tensor((1, 3, 64, 64)		,     rng=random.Random(1      )      ).to(__snake_case      )

      a		:   Union[str, Any]	  =	torch.Generator(device='cpu'      ).manual_seed(0      )
      a		:   str	  =	pipe_a(
          prompt_embeds=__snake_case		,     negative_prompt_embeds=__snake_case		,     image=__snake_case		,     mask_image=__snake_case		,     num_inference_steps=2		,     generator=__snake_case		,     output_type='np'		,     )

      a		:   List[Any]	  =	output.images[0]

      assert image.shape == (64, 64, 3)

      a		:   List[str]	  =	torch.cuda.max_memory_allocated()
      assert mem_bytes < 10 * 10**9

      a		:   Optional[int]	  =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'      )
      assert_mean_pixel_difference(__snake_case		,     __snake_case      )

      # pipeline 2

      _start_torch_memory_measurement()

      a		:   Union[str, Any]	  =	torch.Generator(device='cpu'      ).manual_seed(0      )

      a		:   Optional[int]	  =	floats_tensor((1, 3, 64, 64)		,     rng=random.Random(0      )      ).to(__snake_case      )
      a		:   Tuple	  =	floats_tensor((1, 3, 2_56, 2_56)		,     rng=random.Random(0      )      ).to(__snake_case      )
      a		:   Optional[int]	  =	floats_tensor((1, 3, 2_56, 2_56)		,     rng=random.Random(1      )      ).to(__snake_case      )

      a		:   int	  =	pipe_a(
          prompt_embeds=__snake_case		,     negative_prompt_embeds=__snake_case		,     image=__snake_case		,     mask_image=__snake_case		,     original_image=__snake_case		,     generator=__snake_case		,     num_inference_steps=2		,     output_type='np'		,     )

      a		:   List[str]	  =	output.images[0]

      assert image.shape == (2_56, 2_56, 3)

      a		:   Union[str, Any]	  =	torch.cuda.max_memory_allocated()
      assert mem_bytes < 4 * 10**9

      a		:   Any	  =	load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'      )
      assert_mean_pixel_difference(__snake_case		,     __snake_case      )
def  lowerCamelCase__			(		):
  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()